DIAGNOSTIC PERFORMANCE OF RADIOMICS-BASED ULTRASOUND ANALYSIS IN WOMEN WITH OVULATORY DYSFUNCTION–RELATED ABNORMAL UTERINE BLEEDING
DOI:
https://doi.org/10.37547/Keywords:
abnormal uterine bleeding; radiomics; ultrasound imaging; ovarian morphology; texture analysis.Abstract
Abnormal uterine bleeding associated with ovulatory dysfunction (AUB-O) is a common gynecological condition among women of reproductive age. Radiomics enables quantitative analysis of medical images and allows detection of subtle microstructural tissue characteristics. Objective: To evaluate the diagnostic performance of radiomics-based ultrasound analysis for risk stratification of ovarian changes in patients with ovulatory dysfunction–related abnormal uterine bleeding. Methods: A prospective study included 23 women aged 18–45 years with clinically confirmed ovulatory dysfunction. All participants underwent transvaginal ultrasound examination. Images were stored in DICOM format and processed for radiomics analysis. Results: The mean age of patients was 29.6±4.8 years, and the mean body mass index was 24.3±2.9 kg/m². Ultrasound examination identified follicular cysts in 11 cases, lutein cysts in 3 cases, persistent follicles in 5 cases, and suspicious ovarian morphology in 4 cases. Conventional ultrasound classified 16 cases as benign and 7 as suspicious, while clinical follow-up confirmed 14 benign and 9 high-risk cases. A total of 132 radiomic features were extracted; after feature selection, 8 key parameters were included in the model. Significant predictors included entropy (p=0.004), GLCM contrast (p=0.008), homogeneity (p=0.012), and shape irregularity index (p=0.015). Radiomics-based analysis demonstrated higher diagnostic accuracy compared with conventional ultrasound (p<0.01). Conclusion: Radiomics-based ultrasound analysis improves the diagnostic evaluation of ovarian changes in women with AUB-O and may provide an objective tool for risk stratification and clinical decision-making.
Downloads
References
1.Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a radiomics approach. Nat Commun. 2014;5:4006.
2.Andreotti RF, Timmerman D, Strachowski LM, Froyman W, Benacerraf B, Bennett GL, et al. Ovarian-Adnexal Reporting and Data System (O-RADS): ultrasound risk stratification and management system. Radiology. 2020;294(1):168–185.
3.Bahamondes L, Ali M. Recent advances in managing and understanding abnormal uterine bleeding. F1000Res. 2018;7:F1000 Faculty Rev-684.
4.Dwivedi K, Sharipov A, Ramseela S, Powrnamy P, Mandal A, Waqar D, et al. Evaluating CA-125 for ovarian cancer detection: a review of diagnostic performance. Int J Med Sci. 2025;1(3):116–120.
5.Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017;37(2):505–515.
6.Fraser IS, Langham S, Uhl-Hochgraeber K. Health-related quality of life and economic burden of abnormal uterine bleeding. Expert Rev Obstet Gynecol. 2009;4(2):179–189.
7.Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–577.
8.Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–446.
9.Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics. 2017;37(5):1483–1503.
10.Munro MG, Critchley HOD, Fraser IS. The FIGO classification of causes of abnormal uterine bleeding in the reproductive years. Fertil Steril. 2011;95(7):2204–2208.
11.Park HJ, Lee SM, Kim HJ, et al. Radiomics signature for predicting ovarian tumor type using ultrasound imaging. Ultrasound Med Biol. 2021;47(6):1481–1490.
12.Ravshanovna XM, Dwivedi K. Integration of a comprehensive ultrasound assessment in the prognostic modeling of benign and malignant ovarian tumors. Int J Contemp Pathol. 2024;10(2).
13.Singh S, Best C, Dunn S, Leyland N, Wolfman WL. Abnormal uterine bleeding in pre-menopausal women. J Obstet Gynaecol Can. 2013;35(5):473–475.
14.Valentin L. Imaging in gynecology: best practice in the diagnosis of ovarian masses. Best Pract Res Clin Obstet Gynaecol. 2014;28(5):683–695.
